Device option configuration selection

ABSTRACT

A method for controlling a device capable of performing an operation in response to a job ticket includes comparing the job ticket with options within the capabilities of the device for fulfilling the job. One such device is a printer coupled to a computer for printing text and the like in accordance with such a job ticket. The job configuration and the options defining the plurality of operations may be stored in data structures, such as XML files validated under an XML schema defining options for a universe of similar operations. Each comparison of the job configuration with a device capability option results in a score. These scores are compared to obtain an optimal or preferred option which best approximates the job ticket within the capabilities of the device.

PRIORITY CLAIM

This application is a Continuation of U.S. patent application Ser. No. 10/779,515, filed Feb. 13, 2004, entitled “DEVICE CONTROL USING JOB TICKET SCORING,” the entire contents of which is hereby incorporated by reference herein in its entirety.

TECHNICAL FIELD

The disclosed technology relates to the control of devices such as electronic printers capable of performing operations fulfilled by device capabilities options.

BACKGROUND

Numerous electronic devices are capable of performing operations fulfilled by options selected by a user. For example, some conventional clothes dryers are configured to allow users to choose from pre-defined drying cycles designed for popular fabric types such as cotton, wool or silk. The choice of cycle controls the time, temperature and degree of agitation used in drying the fabric. If a user wishes to dry a fabric which is not among the particular types for which pre-defined cycles have been provided, the user must choose among those cycles the combination of time, temperature and degree of agitation which best approximates the ideal for the fabric which the user wishes to dry.

This observation is true for many peripheral devices for data processing systems and the like. For instance, conventional electronic printers are designed to allow users to print documents in a variety of formats, such as in different paper sizes, color schemes and fonts. Conventional scanners allow users to vary the operations which the scanners perform by selecting different scan areas, pixel densities and output formats. Typically, a peripheral device is controlled by a combination of software found in an operating system resident in the data processing system, such as one of the WINDOWS® brand operating systems available from Microsoft® Corporation of Redmond, Wash., and in “driver” software typically sold with the device itself. Some application packages also include driver software.

Various manufacturers supply devices which perform similar operations but have different capabilities. Devices of the same model may have different capabilities depending on the manner in which their users configure them. For example, users may load particular types of paper into the paper trays of an electronic printer. If a user's preferred paper type is not among those loaded into the paper trays, the user must choose among those paper types available the combination of properties which best approximates those the user desires.

One type of software which controls print jobs by electronic printers includes a data structure having specific fields for entering desired print options. Descriptions of additional capabilities of the device are reduced to indices, that is, to pages organized and tabulated to facilitate search.

Job tickets are used to specify the print options to be used in printing the content of computer files. When a text or image file having a job ticket prepared for one printer is transferred electronically to a recipient, the software controlling the recipient's printer must select the print option available on the recipient's printer which best approximates the print configuration requested in the job ticket. Typically, any combination of desired properties in the job ticket which is not among the options recognized by the software controlling the recipient's printer is reset in whole or in part to default values. Indices are honored only if the model and driver version are identical. As a result, the document printed by the recipient may differ significantly from that printed from the same text or data file by the creator.

The data structures used in conventional printer control software lack interoperability. That is, such software typically defines options in terms of a single naming convention. Such data structures are not easily adapted to serve users defining similar options using different nomenclature.

SUMMARY

The following section of this patent application document presents a simplified summary of the disclosed subject matter in a straightforward manner for readability purposes only. In particular, this section attempts expressing at least some of the general principles and concepts relating to the disclosed subject matter at a relatively high-level simply to impart a basic understanding upon the reader. Further, this summary does not provide an exhaustive or limiting overview nor identify key and/or critical elements of the disclosed subject matter. As such, this section does not delineate the scope of the ensuing claimed subject matter and therefore the scope should not be limited in any way by this summary.

A method and a system are disclosed for controlling devices such as electronic printers capable of performing an operation in response to a job ticket, the operation being fulfilled by device capabilities options. A method and system distinguishes between two types of settings, “parameters” and “features.” “Parameters” are settings having values which do not need to be explicitly enumerated. Typically, parameters have large numbers of possible values. An example of a parameter for an electronic printer is a copy count, which may range from 1 to a large number such as 9999 for certain printers.

Each feature may include one or more options. Each option may include one or more properties which may describe what is offered when that option is selected. For each feature, a user may select one or more options according to his preference. A user may not be permitted to select the individual properties contained within the various options; instead, the user may only select among the prepackaged combinations of properties offered by each option.

A method for controlling the device includes comparing the option(s) requested by a job ticket for a given feature with the available options offered by the device for the corresponding feature. Each available option offered by the device is compared to the option requested by the job ticket and assigned a score depending on how closely they match. The option that most closely matches the intent specified by the job ticket is the one with the highest score.

The device capabilities options can be represented by trees of scored properties and the job ticket specified options can be represented by a tree of desired properties. Each vertex or node on the tree represents an aspect of the operation performed by the device when that particular option is selected. The method may be recursive: scored properties on leaf vertices of the trees of scored properties are compared with corresponding properties of the tree of desired properties first. These comparisons yield scoring factors which are accumulated to obtain scoring factors for the next higher levels of the tree. This process is continued until a final scoring factor for each device capabilities option is obtained. The scoring algorithm used for this comparison and accumulation may be the same at each of the levels of the trees.

The system and method are improvements over prior art techniques. The exemplary method selects the optimal operation by adopting the option closest to the job ticket intent rather than adopting default values in the absence of an exact match.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic representation of an exemplary method for controlling a device;

FIG. 2 is a schematic representation of an exemplary method for controlling a print job;

FIG. 3 is a representative computer system used in implementing components of the exemplary methods illustrated in FIGS. 1 and 2;

FIGS. 4A, 4B and 4C are schematic representations of at least a portion of exemplary scoring processes for use in controlling print jobs;

FIG. 5 is a simplified schematic of an exemplary printer used in illustrating a constraint on the device capabilities options; and

FIG. 6 is a depiction of a print feature hierarchical tree structure used to explain the recursive nature of a print scoring process.

The same reference numerals and/or other reference designations employed throughout the accompanying drawings are used to identify identical components except as may be provided otherwise.

DETAILED DESCRIPTION

The accompanying drawings and this detailed description provide exemplary implementations relating to the disclosed subject matter for ease of description and exemplary purposes only, and therefore do not represent the only forms for constructing and/or utilizing one or more components of the disclosed subject matter. Further, while this description sets forth one or more exemplary operations that may be implemented as one or more sequence(s) of steps expressed in one or more flowcharts, the same or equivalent operations and/or sequences of operations may be implemented in other ways.

FIG. 1 depicts a representative system 1 for controlling a device 2 such as an electronic printer or scanner. The system 1 comprises a computer readable medium 3 including a generalized capabilities data structure 4 and a device capabilities data structure 5. FIG. 2 depicts a representative method 10 for controlling the device 2. The method 10 compares 12 an intended operation specified in a job ticket data structure 6 with options within the capabilities of the device 2. These device capabilities options are stored in the data structure 5. Each comparison of the job ticket with a device capabilities option produces a score. These scores are compared 14 to select an optimal operation within the capabilities of the device 2. Once the optimal operation is selected, the device 2 is signaled 16 to perform that operation.

For ease and clarity of exposition only, the exemplary system and method will be described in terms of controlling the operation of an electronic printer in communication with a data processing system unless otherwise noted. Those skilled in the art will recognize that the disclosed technology and examples are not limited thereto. The disclosed systems and methods are not limited to peripheral devices for data processing systems but can include other electronic equipment. Further, the systems and methods can be used to control any device, or combination of devices, capable of receiving and processing job configurations electronically.

Computer System

FIG. 3 depicts an exemplary data processing system. The system includes a general purpose computing device in the form of a conventional computer 20, including one or more processing units 21, a system memory 22, and a system bus 23 that couples various system components including the system memory 22 to the processing unit 21. The system bus 23 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures.

The system memory 22 includes read only memory (ROM) 24 and random access memory (RAM) 25. A basic input/output system 26 (BIOS), containing the basic routines that help to transfer information between elements within the computer 20, such as during start-up, is stored in ROM 24.

The computer 20 further includes a hard disk drive 27 for reading from or writing to a hard disk, not shown; a magnetic disk drive 28 for reading from or writing to a removable magnetic disk 29; and an optical disk drive 30 for reading from or writing to a removable optical disk 31 such as a CD ROM or other optical media. The hard disk drive 27, magnetic disk drive 28, and optical disk drive 30 are connected to the system bus 23 by a hard disk drive interface 32, a magnetic disk drive interface 33, and an optical drive interface 34, respectively. The drives and their associated computer-readable media provide nonvolatile storage of computer readable instructions, data structures, program modules and other data for the computer 20. Although the exemplary environment described herein employs a hard disk, a removable magnetic disk 29 and a removable optical disk 31, it should be appreciated by those skilled in the art that other types of computer readable media which can store data that is accessible by a computer, such as magnetic cassettes, flash memory cards, digital video disks, Bernoulli cartridges, random access memories (RAMs), read only memories (ROM), and the like, may also be used in the exemplary operating environment.

A number of program modules may be stored on the hard disk, magnetic disk 29, optical disk 31, ROM 24 or RAM 25, including an operating system 35, one or more application programs 36, other program modules 37, and program data 38. A user may enter commands and information into the computer 20 through input devices such as a keyboard 40 and pointing device 42. Other input devices (not shown) may include a microphone, joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 21 through a serial port interface 46 that is coupled to the system bus, but may be connected by other interfaces, such as a parallel port, game port or universal serial bus (USB). A monitor 47 or other type of display device is also connected to the system bus 23 via an interface, such as a video adapter 48. In addition to the monitor 47, personal computers (PCs) typically include other peripheral output devices (not shown), such as speakers and printers.

The computer 20 may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 49. The remote computer 49 may be another PC, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer 20, although only a memory storage device 50 has been illustrated in FIG. 1. The logical connections depicted in FIG. 1 include a local area network (LAN) 51 and a wide area network (WAN) 52. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet.

When used in a LAN networking environment, the computer 20 is connected to the local network 51 through a network interface or adapter 53. When used in a WAN networking environment, the computer 20 typically includes a modem 54 and other means for establishing communications over the WAN 52, such as the Internet. The modem 54, which may be internal or external, is connected to the system bus 23 via the serial port interface 46. In a networked environment, program modules depicted relative to the computer 20, or portions thereof, may be stored in the remote memory storage device. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.

Data Structures

Referring to FIG. 1, a generalized capabilities data structure 4 is an XML schema. Device capabilities data structures 5 and job ticket data structures 6 are XML files including references to the generalized capabilities data structure 4. The generalized capabilities data structure 4 includes a tree of generalized properties. The device capabilities data structure 5 includes a substantially complete set of features representing the device capabilities or functionalities provided by the device, each feature typically containing multiple options, each option representing a state or setting that can be selected for that particular feature, each option containing a tree of scored properties, each scored property describes an aspect of that option. The job ticket data structure 6 includes a set of features, each feature typically containing one option, each option containing a tree of desired properties.

Typically a job ticket is constructed by taking a particular device's device capabilities data structure, and pruning out the unwanted options under each feature, leaving only the desired option for each feature. The trees of scored properties and the tree of desired properties may be expressed in XML validated under the generalized capabilities data structure 4. Validation of the device capabilities data structure 5 and the job ticket data structure 6 are routine procedures within the ordinary skill in the art. The generalized capabilities data structure 4 and the device capabilities data structure 5 may be stored in computer readable memory either in the device 2 itself or in a data processing system in communication with the device.

Scoring Algorithm

FIGS. 4A, 4B, and 4C depict an exemplary method or process 60 for determining an operation score for a device capabilities option. (The flowcharts reproduced in FIGS. 4A, 4B and 4C are joined at nodes 62, 64 and 66.) The job configuration represented by the job ticket data structure (6 in FIG. 1) is compared to each device capabilities option in turn, producing a final score associated with each such option.

The exemplary method 60 is recursive. The method 60 first locates a root node of the tree of scored properties corresponding to the device capabilities option under consideration. The method proceeds 72 to move in a top down, left to right manner through the tree of desired properties to find the lowest-level leaf vertex subordinate to that top-level vertex. The method then determines 74 whether the scored property positioned at that leaf vertex has a corresponding property in a corresponding vertex within the tree of desired properties found in the job ticket. If not, the scoring factor associated with that scored property is set 76 to zero. If the scored property has a corresponding property in the tree of desired properties, the scored property is compared 78 to the corresponding property to obtain a scoring factor for that scored property.

This process is repeated 80 for each scored property on the same level of the tree of scored properties. When scoring factors have been obtained for each property on the same level, the factors are accumulated 82 to obtain a scoring factor for a parent scored property, that is, for the vertex on the next higher level to which the scored properties whose scores were accumulated are subordinate in the tree of scored properties. The method then moves 84 to the level of the parent property and continues the process of obtaining scoring factors. When scoring factors have been obtained for each top-level vertex of the tree of scored properties, the scoring factors associated with the top-level vertices are accumulated 86 to obtain a final score for the a particular option. The method is then repeated for other options.

The technique by which one compares 78 the desired properties with the properties of the device capabilities option is not critical to the disclosed systems and methods. One exemplary technique is binary: one assigns the value one to the scoring factor associated with a scored property if the scored property is substantially identical to the corresponding property of the tree of desired properties found in the job ticket. One assigns the value zero to the scoring factor if the scored factor is not substantially identical to the corresponding job ticket property.

In accordance with another exemplary technique, each desired property, or a subset of the desired properties, is assigned an ordinal number “i” and a numerical weight W_(i). Corresponding properties of the trees of scored properties are assigned the same ordinal numbers. The numerical weights may be stored in the job ticket data structure (6 in FIG. 1). Where the job ticket fails to assign a numerical weight to a particular feature or property, a default weight can be assigned. When comparing the desired option with the equivalent device capabilities option, one assigns the value of the numerical weight W_(i) to the scoring factor associated with the scored property “i” if the scored property “i” is substantially identical to the desired property “i” of the tree of desired properties. One assigns the value zero to the scoring factor if the scored factor “i” is not substantially identical to the desired factor “i”.

In accordance with yet another exemplary technique, a variance function “V_(i)[m_(i)]” is assigned to each desired property or to a subset of the desired properties. For this purpose, “m_(i)” is a measure between the desired property “i” and the scored properties “i.” “V_(i)” is a non-negative, monotone decreasing function of “m” such that V_(i)[0]=1. (A measure is commonly defined as any function defined for any two elements “a” and “b” of a set such that m(a, b)=m(b, a); m(a, b)≧0; m(a, b)=0 if and only if a=b; and m(a, b)+m(b, c)≧m(a, c).). When comparing the desired option with the equivalent option of the array of device capabilities options, one assigns V_(i)[m_(i)] to the scoring factor associated with the scored property “i.” Alternatively, if a numerical weight W_(i) is assigned, one may assign the product W_(i)*V_(i)[m] to the scoring factor.

Where the options are quantifiable, a variance function for use with the foregoing exemplary method is V_(i)[Δ]=k_(i)/[k_(i)+Δ_(i)], where “k_(i)” is a variance factor assigned to the particular feature or property and “Δ_(i)” is the absolute value of the difference between the desired property “i” and the scored property “i”. One example of a feature having quantifiable properties is paper size: where the paper sizes have the same width (e.g., letter and legal sizes), the differences in the lengths serves as measures of the differences between the options. The variance factors “k_(i)” may be stored in the job ticket (6 in FIG. 1). Where the job ticket fails to assign a variance factor, the method either assigns a default value to k_(i) or reverts to assigning zero in the case of a mismatch and either the number one or a numerical weight “W_(i)” n the case of a match

The assignment of numerical weights and variance functions to the desired properties increases the user's control over the selection of the operation to be performed in response to the job ticket. The assignment of numerical weights to the desired properties permits the user to identify which features and properties are most important in determining the optimal operation. Greater numerical weights indicate greater importance in determining which operation to perform. The variance function controls how fast the scoring factor decreases as a function of the mismatch between the scored property and the corresponding properties of the trees of scored properties. Increasing the variance factor “k_(i)” decreases the amount the scoring factor decreases as a function of mismatch. Thus, increasing the variance factor “k_(i)” decreases the importance of an exact match between the scored property and the corresponding property of the tree of desired properties while decreasing the range of properties which will contribute a non-zero, though reduced, scoring factor.

The method by which the scoring factors are accumulated is not critical in the disclosed systems and methods. The method includes performing a linear combination of the scoring factors. One exemplary method is simply to add the scoring factors contributed by the features or properties on the selected level to obtain the final score or a scoring factor for a parent property. Another exemplary method is to multiply the scoring factors SF_(i) by numerical weights W_(i) assigned to the desired properties to obtain a weighted sum

$\sum\limits_{i}\;{{SF}_{i}*{W_{i}.}}$

Another exemplary method is to add the scoring factors to obtain a weighted sum and then to divide the weighted sum by a normalizing factor. Where numerical weights W_(i) are assigned to the desired properties, a normalizing factor is obtained by adding all of the weights associated with the desired features or properties on the selected level, thereby obtaining a normalized sum:

$\frac{\sum\limits_{i}\;{{SF}_{i}*W_{i}}}{\sum\limits_{i}\; W_{i}}$ The latter method assures that the scoring factors of parent properties will not be unduly weighted in subsequent accumulation steps merely because those scoring factors were derived from adding the scoring factors of subproperties.

Conventional electronic printers are subject to constraints which prevent the printers from performing operations fulfilled by certain device capabilities options. Such a constraint may be handled by the method or process in alternative ways. Constrained options may be excluded a priori from the scoring process. Alternatively, the operation scores or final scores obtained by the method may be stored in memory. The device capabilities option having the most score is checked first against the constraints on the device; then the option having the next most score is checked against the constraints; and then the remaining combinations are checked in the same order as needed. Once a device capabilities option having the highest score and which is not subject to any constraint, that option is used to perform the print operation.

One such constraint is illustrated in FIG. 5. A printer 90 depicted schematically in FIG. 5 includes an upper paper tray 92 and a lower paper tray 94. Rollers 96 and 98 pick sheets of paper from the paper trays 92, 94 for printing in response to job tickets. In the printer 90 depicted in FIG. 5, paper picked from the upper paper tray 92 follows a relatively straight path through a nip 100 toward the print head (not shown), as indicated by the arrows 102, 104 and 106. Paper picked from the lower paper tray 94 follows a more serpentine path: the paper passes through a nip 110; is bent over a roller 112; is bent again over rollers 114 and 116; and presses against a guide 118 to direct the paper into the nip 100, as indicated by the arrows 120, 122, 124, 126 and 104.

The manufacturer of the printer 90 may specify or pre-program a constraint which allows the printer to draw lightweight paper 130 from either the upper or lower paper tray 92, 94, but refuses a job ticket that draws heavier weight paper 132 from the lower paper tray 94. Since paper from the lower paper tray 94 follows a path 120, 122, 124, 126, 104, 106 toward the print head (not shown) which is more serpentine than the path 102, 104, 106 followed by paper from the upper paper tray 92, stiffer, heavier weight paper 132 is more likely to jam when picked from the lower paper tray 94 than when picked from the upper paper tray 92.

For example, if the only constraint on the printer 90 is that heavier weight paper 132 cannot be drawn from the lower paper tray 94, each device capabilities option for the printer 90 will be checked against the constraint in order according to the preference of the final score associated with each such option, from most to least preferred. The device capabilities option which has the most preferred final score among those options which do not specify both the use of heavier weight paper 132 and the use of paper drawn from the lower paper tray 94 will have the highest score.

EXAMPLES

The following examples are illustrative only and are not intended to limit the scope or range of the disclosed systems and methods.

Example 1

Suppose that a generalized capabilities data structure for a class of electronic printers includes the tree of generalized properties subordinate to the feature “MediaType” 140 which is illustrated in FIG. 6. According to this scheme, “MediaType” 140 has the following properties:

-   -   MediaWeight (142): Light (20 lb.), Medium (24 lb.), Heavy (28         lb.), Ultra Heavy (32 lb.)     -   Color (144): Transparent, White, Red, Green, Blue     -   ColorSaturation (146): None, Light, Medium, Intense     -   Texture (148): Glossy, Bond, ClayCoated     -   HoleTreatment (150):         -   LeftEdge (152): None, TwoHoleLeftEdge, ThreeHoleLeftEdge         -   TopEdge (154): None, TwoHoleTopEdge     -   PrePrinted (156): Plain, Letterhead, Watermark, Borders

Consider the following job ticket:

-   -   MediaWeight: Medium         -   (Weight=100)         -   (VarianceFactor=5)     -   Color: White         -   (Weight=100)     -   ColorSaturation: None         -   (Weight=100)     -   Texture: Bond         -   (Weight=100)     -   HoleTreatment:         -   (Weight=200)         -   LeftEdge: None             -   (Weight=1000)         -   TopEdge: None             -   (Weight=500)     -   PrePrinted: Plain         -   (Weight=100)

Let the electronic printer support the following three MediaType 140 options:

-   -   MediaWeight: Heavy     -   Color: Transparent     -   ColorSaturation: None     -   Texture: Glossy     -   HoleTreatment:         -   LeftEdge: ThreeHoleLeftEdge         -   TopEdge: None     -   PrePrinted: Plain     -   MediaWeight: Medium     -   Color: Blue     -   ColorSaturation: Light     -   Texture: ClayCoated     -   HoleTreatment:         -   LeftEdge: None         -   TopEdge: None     -   PrePrinted: Borders     -   MediaWeight: Light     -   Color: White     -   ColorSaturation: None     -   Texture: Bond     -   HoleTreatment:         -   LeftEdge: None         -   TopEdge: None     -   PrePrinted: Plain

The optimal MediaType 140 option will be determined by comparison of the job ticket with the three device capabilities options. More specifically, the scoring factor at each level of the hierarchy of features and properties will be determined by the scoring algorithm:

$\frac{\sum\limits_{i}\;{{SF}_{i}*W_{i}}}{\sum\limits_{i}\; W_{i}}$ where “SF_(i)” is the scoring factor associated with an option or property having subproperties and “W_(i)” is a weighting factor. The same scoring algorithm is repeated on each level of the trees of scored properties.

It is noted that the only property among those defined in the generalized capabilities having subproperties is the “HoleTreatment” property 150. Comparing the desired properties with the scored properties corresponding to the first device capabilities option, it is noted that the desired property subordinate to the “LeftEdge” property 152 is “None,” while the corresponding property in the tree of scored properties subordinate to the “LeftEdge” property 152 is “ThreeHolesLeftEdge.” Thus, the scoring factor for the “LeftEdge” 152 property is zero. On the other hand, both the desired subproperty and the corresponding subproperty under the “TopEdge” property 154 are “None.” It follows that the scoring factor for the “Top Edge” property 154 is 1.

The scoring factor for the “HoleTreatment” property 150 is obtained by multiplying the scoring factors by the numerical weights assigned to the “LeftEdge” 152 and “TopEdge” 152 properties, respectively; adding the scoring factors for the “LeftEdge” and “TopEdge” properties 152, 154 to obtain a weighted sum (1000*0+500*1=500); adding the numerical weights of the “LeftEdge” and “TopEdge” properties 152, 154 to obtain a normalizing factor (1000+500=1500); and dividing the weighted sum by the normalizing factor to obtain the scoring factor (500/1500=0.33).

Continuing with the comparison, it is noted that the job ticket specifies a variance factor of “5” for the MediaWeight property 142 based on the weights of the papers specified in the generalized capabilities data structure. The desired subproperty for the “MediaWeight” property 142 is “Medium” (24 lb.). The corresponding subproperty in the tree of scored properties is “Heavy” (28 lb.). Thus, the scoring factor associated with the MediaWeight property 142 is k_(i)/[k_(i)+Δ]=5/[5+|24-28|]=0.56. It is noted that the scoring factor associated with the MediaWeight property 142 is non-zero even though there is a variation between the desired property and the corresponding property of the tree of scored properties.

Continuing on the same manner, one obtains the following scoring factors:

-   -   MediaWeight: 0.56     -   Color: 100     -   ColorSaturation: 100     -   Texture: 0     -   HoleTreatment: 0.33     -   PrePrinted: 100         Multiplying these scoring factors by the respective weights and         adding, one obtains a final (non-normalized) score of         100*0.56+100*1+100*1+100*0+200*0.33+100*1=422.

Similarly, comparison of the desired options with the second set of device capabilities options yields an operation score (non-normalized) of 300. Comparison of the desired options with the third set of device capabilities options yields a final score of 656. Since the comparison of the desired options with the third set of device capabilities options yields the largest operation score, the MediaType 140 represented by the third set of device capabilities options is the optimal MediaType 140 even though it does not identically match the desired options. Note, if normalization has been done the same “winning” option would have been determined but with a different score between 0 and 1.

Thus, where the job ticket presented with a print job sent to an electronic printer does not match identically an option within the capabilities of the printer, the exemplary system and method provide for selection of the optimal operation within the printer's capabilities which most closely matches the user's intent. The comparison between the job ticket and the options within the capabilities of the printer is facilitated by the generalized capabilities data structure (4 in FIG. 1) which provides a common framework and vocabulary for the comparison.

Example 2

The interoperability of the exemplary system and method will be illustrated by the following example. Assume that an institution has created standard definitions of MediaSize (e.g., paper size) properties. Let the standard MediaSize property contain the following options (expressed in XML code):

< PROPERTY> <Name> <MediaSizeX> <MediaSizeY> < PROPERTY>

Now assume that there exist other clients that use another system of referring to media sizes. This system describes media sizes using a different naming convention, which will be referred to as the “A Name” system. Let there be another group of clients that use a second naming convention, which will be referred to as the “B Name” system.

The exemplary system and method allow devices to accommodate these client's naming conventions by simply adding the “A Name” system and the “B Name” system as additional subproperties for the MediaSize property:

<PROPERTY> <Name> <MediaSizeX> <MediaSizeY> <AName> <BName> </PROPERTY>

Thus, if a client requests a media size by referencing only the “A Name,” he automatically will get the correct media size.

< PROPERTY> <AName> </ PROPERTY>

In order to ensure that the “A Name” produces a good match even on devices that do not recognize the “A Name” property, the weighting of the “A Name” property can be increased to ensure that the property is given precedence during the scoring process:

< PROPERTY> <Name> <MediaSizeX> <MediaSizeY> <AName Weight=100> </ PROPERTY>

While particular examples and possible implementations have been called out above, alternatives, modifications, variations, improvements, and substantial equivalents that are or may be presently unforeseen may arise to applicants or others skilled in the art. Accordingly, the appended claims as filed, and as they may be amended, are intended to embrace all such alternatives, modifications, variations, improvements, and substantial equivalents. Further, the recited order of processing elements or sequences, or the use of numbers, letters, or other designations therefore, is not intended to limit the claimed process to any order except as may be specified in the claims. 

1. A computer-implemented method comprising: identifying one or more alternative device options that are available when one or more initially selected device options are unavailable and which substantially approximates the initially selected device options based on a scoring mechanism; and performing the at least one requested device operation using the identified alternative device options.
 2. The computer-implemented method as set forth in claim 1 wherein at least one of either the at least one requested device operation or the one or more initially selected device options are defined in at least one job ticket data structure.
 3. The computer-implemented method as set forth in claim 2 where the at least one job ticket data structure comprises at least one of either one or more desired device options or one or more selected feature options corresponding to desired device options, or any other information indicating desired device operation constraints.
 4. The computer-implemented method as set forth in claim 1 wherein the scoring mechanism comprises selecting at least one optimal device option that satisfies at least one scoring criterion.
 5. The computer-implemented method as set forth in claim 4 wherein selecting at least one optimal device option that satisfies at least one scoring criterion further comprises: comparing one or more initially selected device options with one or more of the alternative device options that are available.
 6. A computer-readable medium having stored thereon at least one data structure corresponding to a schema for defining relations between devices options, the computer readable medium comprising: one or more device capability structures that describe device features available for performing at least one device operation; at least two feature structures that describe options available in connection with an associated feature selected for at least one requested operation; and one or more option structures associated with the at least two feature structures that describe at least one property of the associated feature options.
 7. The computer-readable medium as set forth in claim 6 wherein the one or more feature option properties are scored based on a scoring mechanism with respect to at least one desired option of an associated feature selected for at least one requested operation.
 8. The computer-readable medium as set forth in claim 6 further comprising at least one job ticket data structure that specifies at least one of either one or more desired device options or any other information indicating desired device operation.
 9. The computer-readable medium as set forth in claim 6 wherein the one or more feature option properties are associated with one or more scoring factor structures that receive at least one of either a weight value, a binary value, a variance factor value, a default value, or any other value that expresses the relative importance of at least one desired option for at least one requested operation.
 10. The computer-readable medium as set forth in claim 9 wherein the variance factor value influences the degree to which one or more option score values, which result from one or more applied scoring factors defined in the one or more scoring factor structures, changes responsive to at least one of either matches, mismatches, or any other basis for comparison between the available options defined in the at least two feature structures and the at least one desired option.
 11. The computer-readable medium as set forth in claim 10 wherein influencing the degree to which one or more option score values changes responsive to at least one of either matches, mismatches, or any other basis for comparison, expresses the importance of achieving exact matches between the available options and the at least one desired option.
 12. The computer-readable medium as set forth in claim 6 further comprising one or more scoring factor structures, which define one or more scoring factors that can be applied on desired device operation intention information reflecting the relative importance of one or more selected features to obtain one or more score values for the selected features.
 13. The computer-readable medium as set forth in claim 12 wherein the scoring factors achieve the score values based on at least one of either weight values provided for the selected features, binary values indicating selection of the selected options, calculated values resulting from mathematical relationships between the desired device operation intention information and the available options, or any combination of the weight, binary and/or calculated values.
 14. The computer-readable medium as set forth in claim 13 wherein the mathematical relationships are expressed as a variance function comprising V_(i)[m_(i)] with V_(i) representing a non-negative, monotone decreasing function, such that V_(i [)0]=1, and with m_(i)representing a measure between the desired device operation intention information and the available options.
 15. The computer-readable medium as set forth in claim 13 wherein the mathematical relationships are expressed as a variance function comprising k_(i)/[k_(i) +Δ_(i)] with k_(i) representing variance factor values forming at least a portion of the desired device operation intention information and with Δ_(i) representing absolute values of the differences between quantified available option values and quantified desired property values forming at least another portion of the desired device operation intention information.
 16. The computer-implemented method as set forth in claim 1 further comprising applying at least one scoring factor to at least one comparison result value obtained from comparing one or more initially selected device options with one or more of the alternative device options to obtain one or more scoring result values.
 17. The computer-implemented method as set forth in claim 16 wherein applying at least one scoring factor further comprises basing the one or more scoring result values on at least one of either weight values provided for the initially selected device options, binary values indicating selection of initially selected device options, calculated values resulting from mathematical relationships between the initially selected device options and the alternative device options, or any combination of the weight, binary and/or calculated values.
 18. A computer-readable medium having stored thereon at least one data structure corresponding to a schema for defining relations between corresponding devices options associated with one or more devices, the computer readable medium comprising: one or more device operation structures that define one or more properties available for selection in connection with performing at least one requested device operation; one or more alternative device operation structures that define one or more device operation properties recognized by one or more operation requesting devices using one or more different naming conventions and that identify one or more corresponding properties defined in the one or more device operation structures; and one or more scoring factor structures associated with the one or more alternative device operation structures.
 19. The computer-readable medium as set forth in claim 18 wherein the one or more scoring factor structures further comprise one or more scoring factors that can be applied to the one or more device operation property definitions.
 20. The computer-readable medium as set forth in claim 19 wherein the one or more scoring factors comprises at least one weight value or any other any other information indicating desired device operation selection intents. 